Monday, 11 October 2021
14h00 – 15h30

 Salle 109 – Bâtiment 9
Université de Montpellier – Campus Triolet

Accounting for spatial sampling patterns in Bayesian phylogeography.

 Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing geo-referenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a new Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of geo-referenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.

Hybrid Seminar, in situ and online:

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